Method, Apparatus and System of Intelligent Navigation

ABSTRACT

The present disclosure describes a method, an apparatus and a system of intelligent navigation. In one embodiment, a method includes: receiving a user inquiry from a client terminal; searching a navigation dictionary based on the user inquiry to obtain a recommendation result corresponding to the user inquiry, the navigation dictionary including an editor recommendation based on user behavior information; and sending the recommendation result to the client terminal. The present disclosure can enhance the accuracy, relevancy, richness and intelligence of the intelligent navigation, and reduce user search time as well as the search loading on the server.

CROSS REFERENCE TO RELATED PATENT APPLICATIONS

This application is a national stage application of an internationalpatent application PCT/US11/22131, filed Jan. 21, 2011, which claimspriority from Chinese Patent Application No. 201010199106.0, filed Jun.12, 2010, entitled “Method, Apparatus and System for Smart Navigation,”which applications are hereby incorporated in their entirety byreference.

TECHNICAL FIELD

The present disclosure relates to the field of Internet technology and,more specifically, to a method, apparatus and system of intelligentnavigation.

BACKGROUND OF THE PRESENT DISCLOSURE

When a user carries out on-line shopping, he/she typically starts fromsearching for products on e-commerce websites. A user inputs certainkeywords corresponding to his/her desires and the returned search resultwould typically include recommended categories of products and a list ofproducts. In particular, the categories of products may includefront-end categories and back-end categories. The front-end categoriesare for user interface (UI) presentation, while the back-end categoriesare for management of products. Currently, categorization of products inmainstream systems is generally presented in a tree-like structure inwhich each parent-category has a plurality of sub-categories, while eachsub-category has only one parent-category. Moreover, the scopes of thecategories decrease from top of the tree towards the bottom of the tree.

In the e-commerce websites of earlier days, the recommended categorieswere determined according to the number of products returned in thesearch result under the keyword used by the user, and were typicallyshown in hierarchies. However, with the rapid increase in the number ofproducts, when a user inputs certain keywords, the number of categorieshas increased dramatically and as a result the user may have difficultyin deciding under which category or categories he/she should conduct arefined search. To solve the above problem, one approach is to score therelevance of categories according to past history of user behavior ofclicking on the various categories and to present the categoriesdynamically based on the scores. The relevance of categories typicallydecreases from left to right and the categories with less relevant maybe hidden by category folding. However, the above approach stillpresents the search results starting from the first-level categories,and consequently the user needs to click many times to screen theresults into finer categories. For example, when a user inputs thekeyword “T-shirt”, the search result may present first-level categoriessuch as “women's wear”, “men's wear” and “others.” As such, the userneeds to click on one of the first-level categories, e.g., “women'swear”, in order to view refined categories such as “short-sleeveT-shirt,” “couple's T-shirt,” “cotton T-shirt,” etc.

In order to shorten search time, most e-commerce websites nowadays useintelligent navigation techniques to facilitate a user search.Intelligent navigation employs a bottom-up approach for recommendation,which considers all the factors such as number of clicks, purchases ofproducts of a certain category, and number of products corresponding tothe keywords, and provides the categories or features which are mostrelevant according to a certain recommendation algorithm. When thenumber of clicks, purchases or products of a certain category, or numberof products reaches a given threshold, the bottom-up process is stopped.However, certain drawbacks still exist in the above approach whichobtains the recommended categories by calculating data relating to theuser. The drawbacks include factors like: noise interference in the dataof user behavior and misplacement of one or more categories affectingthe accuracy of the recommended categories; the recommended categoriesbeing not rich enough when the data of user behavior related to thekeyword is small; and the inability to provide recommendation when thenumber of clicks corresponding to the keyword is lacking.

To solve the aforementioned drawbacks, artificial factors can be addedinto the recommended categories by intelligent navigation when improvingthe recommendation algorithm. Conventional techniques mainly implementartificial interference of intelligent navigation by editing therecommended terms. Website operators write the keywords that need to beedited and the recommended categories into a text document in apre-defined format, integrate the artificial data with the datarecommended by the algorithm, and save the integrated data in a serverused for the intelligent navigation.

The following problems, however, still exist in the conventionaltechniques: the artificial interference of intelligent navigation lacksa user-friendly interface; propensity of errors in recommendations dueto the recommendations being made based on the experience of websiteoperators without data support; and the artificial interference ofintelligent navigation lacking feedback from users and hence theinability to trace the effect of the artificial interference.

SUMMARY OF THE DISCLOSURE

The present disclosure introduces a method and an apparatus ofintelligent navigation for enhancing the relevancy, the richness, andthe intelligence of intelligent navigation.

In one aspect, a method of intelligent navigation is provided. Themethod comprises: receiving a user inquiry from a client terminal;searching a navigation dictionary based on the user inquiry to obtain arecommendation result corresponding to the user inquiry, the navigationdictionary including an editor recommendation based on user behaviorinformation; and sending the recommendation result to the clientterminal.

In one embodiment, the user inquiry may comprise a search keyword, andthe editor recommendation may comprise an index keyword andrecommendation content.

In one embodiment, searching the navigation dictionary based on the userinquiry to obtain the recommended result corresponding to user inquirymay comprise: searching for the editor recommendation in the navigationdictionary based on the search keyword; obtaining the recommendationcontent corresponding to the search keyword; and providing therecommendation content as the recommendation result.

In one embodiment, the user inquiry may comprise a search keyword and asearch category, and the editor recommendation may comprise an indexkeyword, an index category and recommendation content.

In one embodiment, searching the navigation dictionary based on the userinquiry to obtain the recommended result corresponding to user inquirymay comprise: searching for the editor recommendation in the navigationdictionary based on the search keyword and the search category;obtaining the recommendation content corresponding to the search keywordand the search category; and providing the recommendation content as therecommendation result.

In one embodiment, the user inquiry may comprise a search keyword, andthe editor recommendation may comprise an index keyword, a recommendedtype, and recommendation content.

In one embodiment, searching the navigation dictionary based on the userinquiry to obtain the recommended result corresponding to user inquirymay comprise: searching for the editor recommendation in the navigationdictionary based on the search keyword; obtaining the recommendationtype and recommendation content corresponding to the search keyword; andproviding the recommendation type and recommendation content as therecommendation result. In one embodiment, after sending therecommendation result to the client terminal, the method may furthercomprise: displaying the recommendation content on a user interfacebased on a format according to the recommendation type.

In one embodiment, before searching the navigation dictionary based onthe user inquiry, the method may further comprise: obtaining the userbehavior information; providing a user behavior log based on the userbehavior information; generating reference data based on statistics inthe user behavior log; compiling the reference data to obtain the editorrecommendation corresponding to the user behavior information; andcompiling the editor recommendation into the navigation dictionary.

In one embodiment, the user behavior log may comprise a search log, andthe reference data may comprise a keyword, a search category, and searchdata.

In one embodiment, compiling the reference data to obtain the editorrecommendation corresponding to the user behavior information maycomprise: when the search data of the reference data is greater than apredetermined search threshold, generating the editor recommendationwhich includes an index keyword, an index category, and recommendationcontent based on the reference data, wherein the index keyword comprisesa keyword of the reference data, the index category comprises a searchcategory of the reference data, and the recommendation content comprisesa category, an attribute, or the category and attribute corresponding tothe search data having a greatest value in the reference data.

In one embodiment, the user behavior log may comprise a clicking log,and the reference data may comprise a keyword, a search category, andclicking data.

In one embodiment, compiling the reference data to obtain the editorrecommendation corresponding to the user behavior information maycomprise: when the clicking data of the reference data is greater than apredetermined clicking threshold, generating the editor recommendationwhich includes an index keyword, an index category, and recommendationcontent based on the reference data, wherein the index keyword comprisesa keyword of the reference data, the index category comprises a searchcategory of the reference data, and the recommendation content comprisesa category, an attribute, or the category and attribute corresponding tothe clicking data having a greatest value in the reference data.

In one embodiment, the user behavior log may comprise a purchase log,and the reference data may comprise a keyword, a search category, andpurchase data.

In one embodiment, compiling the reference data to obtain the editorrecommendation corresponding to the user behavior information maycomprise: when the purchase data of the reference data is greater than apredetermined purchase threshold, generating the editor recommendationwhich includes an index keyword, an index category, and recommendationcontent based on the reference data, wherein the index keyword comprisesa keyword of the reference data, the index category comprises a searchcategory of the reference data, and the recommendation content comprisesa category, an attribute, or the category and attribute corresponding tothe purchase data having a greatest value in the reference data.

In one embodiment, after compiling the reference data to obtain theeditor recommendation corresponding to the user behavior information,the method may further comprise: obtaining user feedback informationcorresponding to the editor recommendation; determining whether or notthe editor recommendation is valid; keeping the editor recommendation ifit is determined to be valid; and modifying the editor recommendation ifit is determined to be invalid.

In one embodiment, the user feedback information may comprise a numberof clicks corresponding to the editor recommendation and a number ofclicks of a given category, and when the number of clicks correspondingto the editor recommendation is smaller than the number of clicks of thegiven category, the editor recommendation information may be determinedto be valid.

In another aspect, an intelligent navigation server is provided. Theserver comprises: an obtaining module that receives a user inquiry froma client terminal; an inquiry module that searches a navigationdictionary based on the user inquiry to obtain a recommendation resultcorresponding to the user inquiry, the navigation dictionary includingan editor recommendation which is obtained based on user behavior; and atransmission module that sends the recommendation result to the clientterminal.

In yet another aspect, an intelligent navigation system is provided. Thesystem comprises: a navigation dictionary including an editorrecommendation based on user behavior information; a webpage server thatreceives a user inquiry from a client terminal, sends the user inquiryto an intelligent navigation server, receives a recommendation resultfrom the intelligent navigation server, and sends the recommendationresult to the client terminal; and the intelligent navigation serverthat receives the user inquiry information from the webpage serversearches the navigation dictionary based on the user inquiry to obtainthe recommendation result corresponding to the user inquiry information,and sends the recommendation result to the webpage server.

In one embodiment, the system may further comprise: a log server thatobtains the user behavior information from the webpage server, generatesa user behavior log based on the user behavior information, and sendsthe user behavior log to a storage calculation platform; the storagecalculation platform stores the user behavior log, generates referencedata based on statistics in the user behavior log, calculates analgorithmic recommendation using an intelligent navigation algorithm,provides the reference data and algorithmic recommendation to a back-enddatabase, integrates the editor recommendation and the algorithmicrecommendation to provide integrated data, compiles the integrated datainto the navigation dictionary, and imports the navigation dictionaryinto the intelligent navigation server; and an editing device that editsthe reference data, obtains the editor recommendation corresponding touser behavior information, and imports the editor recommendation intothe back-end database.

The present disclosure provides a number of advantages. As therecommendation results include the editor recommendation which isobtained based on information relating to the user behavior, therelevancy, richness and intelligence of the intelligent navigation maybe enhanced. The negative effects on the recommendation algorithm causedby noise, lack of clicking information and misplacement of productsunder wrong categories may be minimized and, accordingly, the usershopping experience may be improved. As the recommendation data isprovided by analyzing user behavior information and checking the numberof products, coupled with the ability to track the effect of the editednavigation data, the accuracy, relevancy, richness and intelligence ofthe recommendation results are guaranteed. Accordingly, user search timedecreases, and search loading on the servers is reduced. Of course, anyproduct implementing an embodiment of the present disclosure needs notto possess all the aforementioned advantages at the same time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of a method of intelligent navigation inaccordance with a first embodiment of the present disclosure.

FIG. 2 is a diagram of a system of intelligent navigation in accordancewith a second embodiment of the present disclosure.

FIG. 3 is a flowchart of a method of intelligent navigation inaccordance with the second embodiment of the present disclosure.

FIG. 4 is a diagram of a category tree of products in accordance withthe second embodiment of the present disclosure.

FIG. 5 is a diagram of categories shown in a user interface of a clientterminal in accordance with the second embodiment of the presentdisclosure.

FIG. 6 is a diagram of parent-categories and sub-categories shown in theuser interface of a client terminal in accordance with the secondembodiment of the present disclosure.

FIG. 7 is a diagram of attributes shown in the user interface of aclient terminal in accordance with the second embodiment of the presentdisclosure.

FIG. 8 is a block diagram of an intelligent navigation server inaccordance with a third embodiment of the present disclosure.

FIG. 9 is a block diagram of an intelligent navigation in accordancewith a fourth embodiment of the present disclosure.

FIG. 10 is a block diagram of another intelligent navigation inaccordance with the fourth embodiment of the present disclosure.

DETAILED DESCRIPTION

The following is a clear and complete description of embodiments withreference to the figures. It shall be understood that the disclosedembodiments constitute merely some, but not all, of the exemplaryimplementations of the proposed technique. Any variations ormodifications of the disclosed embodiments by one with ordinary skill inthe art are still within the scope of protection the present disclosureis entitled to.

FIG. 1 illustrates a flowchart of a method of intelligent navigation inaccordance with an embodiment of the present disclosure. The methodincludes a number of operations as described below.

At 101, a server receives a user inquiry from a client terminal

The user inquiry may include one or more inquiry keywords, and mayfurther include one or more inquiry categories.

At 102, the server searches a navigation dictionary based on the userinquiry to obtain one or more recommendation results corresponding tothe user inquiry.

The navigation dictionary includes information relating to one or morerecommendations by the editor corresponding to user behaviorinformation.

Before searching the navigation dictionary based on the user inquiry,information relating to the user behavior may be obtained and stored ina user behavior log. In one embodiment, information relating to the userbehavior may include inquiry keywords inputted by the user in a searchbox, products and their categories and attributes that were clicked onby the user after conducting a search based on the inquiry keywords,products purchased by the user and their categories and attributes, andrecommended categories and all sub-categories clicked on by the userafter conducting a search based on the inquiry keywords.Correspondingly, the user behavior log may include a search log, aproduct clicking log, a purchase log and a navigation area clicking log.

When the user clicks on a search button after entering one or moreinquiry keyword in a search box, a log server generates a search record.The text form of the search record forms the search log, which is usedto describe the categories when the user conducts the keyword search.

When the user clicks on a product on a search result page, the logserver generates a product clicking record. The text form of the productclicking record forms the product clicking log, which is used todescribe the product clicked on by the user after conducting the keywordsearch as well as information relating to and attributes of the product.

When the user purchases a certain product, the log server generates apurchase record. The text form of the purchase record forms the purchaselog, which is used to describe the user purchase of a certain product.

When the user clicks on a certain recommended category or one categoryof all categories after searching, the log server generates anintelligent navigation clicking record or an all-category clickingrecord the respective text form of which forms the navigation areaclicking log.

After generating the user behavior log, statistics, or reference data,may be generated based on information recorded in the user behavior log.In one embodiment, the reference data may include the user searchrecord, the product clicking record, the navigation area clickingrecord, the purchase record and the information relating to the categorytree of products. The statistics of the distribution of the searchcategory, clicked product categories and purchases corresponding to eachinquiry keyword may be generated regularly, and distribution statisticsmay be shown according to the hierarchy of a category tree.

In one embodiment, the keywords recorded in the search log may benormalized (including, for example, conversion between capital and smallcases, full/half-width conversion for entry of Chinese characters,punctuation conversion, conversion of traditional/simplified Chinesecharacter fonts, etc.), and the statistics of the search page view (PV)and search user view (UV) of each keyword in different categories may begathered. The keywords recorded in the product clicking log may benormalized, and the statistics of the clicking PV and the clicking UV ofeach keyword in different categories may be generated. The statistics ofthe purchase log may be generated to calculate transaction UV, thenumber of transactions and the monetary amount of transactions of eachkeyword in different categories. Then, the combination “keyword+searchcategory” may be used as a key to intersect the data derived fromprocessing the search log, the product clicking log and the purchaselog, and to obtain reference data in the form of “keyword+searchcategory+search PV+search UV+clicking PV+clicking UV+transactionUV+number of transactions+monetary amount of transactions”, or in asimilar format, as shown in Table 1.

TABLE 1 Format of Reference Data Monetary Search Transaction Number ofAmount of Keyword Category Search PV Search UV Clicking PV Clicking UVUV Transactions Transactions

After obtaining the reference data, information relating to editorrecommendations corresponding to information relating to the userbehavior may be obtained by compiling the reference data.

When the user behavior log corresponding to information relating to theuser behavior is the search log, the reference data may includekeywords, search categories and search data, and the search data may bethe search PV and/or the search UV. When the value of the search dataincluded in the reference data is greater than a predetermined searchthreshold, each or at least one editor recommendation including one ormore index keywords, one or more index categories and recommendationcontent may be generated based on the reference data. In particular, theone or more index keywords may be the keywords of the reference data,the one or more index categories may be the search categories of thereference data, and the content of the recommendations may be thecategories and/or attributes corresponding to the search data of thereference data having the largest value (the value of the search PVand/or search UV being the greatest). The search threshold may be avalue set by the system, or may be modified based on actual conditions.

When the user behavior log corresponding to information relating to theuser behavior is the clicking log, the reference data may includekeywords, search categories and clicking data, and the clicking data maybe the clicking PV and/or the clicking UV. When the value of theclicking data included in the reference data is greater than apredetermined clicking threshold, editor recommendation including one ormore index keywords, one or more index categories and content of therecommendations may be generated based on the reference data. Inparticular, the one or more index keywords may be the keywords of thereference data, the one or more index categories may be the searchcategories of the reference data, and the content of the recommendationsmay be the categories and/or attributes corresponding to the clickingdata of the reference data having the largest value. The clickingthreshold may be a value set by the system, or may be modified based onactual conditions.

When the user behavior log corresponding to information relating to theuser behavior is the purchase log, the reference data may includekeywords, search categories and purchase data, and the purchase data maybe the transaction UV, the number of transactions and/or the monetaryamount of transactions. When the value of the purchase data of thereference data is greater than a predetermined purchase threshold,editor recommendation including one or more index keywords, one or moreindex categories and content of the recommendations may be generatedbased on the reference data. The one or more index keywords may be thekeywords of the reference data, the one or more index categories may bethe search categories of the reference data, and the content of therecommendations may be the categories and/or attributes corresponding tothe purchase data of the reference data having the largest value. Thepurchase threshold may be a value set by the system, or may be modifiedbased on actual conditions.

In one embodiment, after obtaining the editor recommendations, theeditor recommendations may be compiled into the navigation dictionary.Alternatively, the obtained editor recommendations may be integratedwith the algorithmic recommendation, which is computed by the currentintelligent navigation algorithm, and the integrated data may becompiled into the navigation dictionary. If the editor recommendationand the algorithmic recommendation are identical, only one of them iskept; otherwise, both of them are kept.

In another embodiment, after obtaining the editor recommendation,information relating to user feedback corresponding to the editorrecommendation may be obtained and used to determine whether the editorrecommendation is valid. If the editor recommendation is valid, it willbe kept; otherwise, it will be modified. Since the editor recommendationis compiled into the navigation dictionary as the category and/orattribute having the largest number of clicks, it will be modifiedaccordingly if the number of clicks of the editor recommendation doesnot meet a certain requirement. In one embodiment, information relatingto user feedback may include the number of clicks corresponding to theeditor recommendation and the number of clicks of each category. Whenthe number of clicks corresponding to the editor recommendation is lessthan the number of clicks of a certain category, e.g., indicating thecategory of the editor recommendation is not the category having thelargest number of clicks, the editor recommendation is deemed to beinvalid.

When searching the navigation dictionary based on the user inquiry, theeditor recommendation may include index keywords and content of therecommendation corresponding to the index keywords if the user inquiryincludes search keywords. The content of the recommendation may includerecommended categories and/or recommended attributes. Table 2 shows anexample of editor recommendation.

TABLE 2 Index Keywords Recommendation content T-shirt Women'slong-sleeve T-shirt; Women's short-sleeve T-shirt; Men's long-sleeveT-shirt; Men's short-sleeve T-shirt; Children's T-shirt; sports T-shirt;couple's T-shirt . . . . . .

During the search of the navigation dictionary, matching between thesearch keywords and the index keywords of the navigation dictionary maybe performed, and the editor recommendation corresponding to eachmatched index keyword may be obtained and used as the resultantrecommendation. The editor recommendation tends to have more relevanceand accuracy as it is obtained based on the statistics of a large amountof user behavior information. Accordingly, by searching the navigationdictionary to obtain recommendations, the relevancy and accuracy of therecommendations may be effectively enhanced and the number of userinquiries may be reduced, thereby lessening the loading on the servers.

In the event that the user inquiry includes search keywords and searchcategories, the editor recommendation may include index keywords, indexcategories and recommendation content corresponding to the indexkeywords and index categories. The recommendation content may includerecommended categories and/or recommended attributes. The editorrecommendation including index keywords, index categories andrecommendation content are shown in Table 3.

TABLE 3 Index Index Keywords Categories Recommendation content T-shirtRoot category Women's long-sleeve T-shirt; Women's short-sleeve T-shirt;Men's long-sleeve T-shirt; Men's short-sleeve T-shirt; Children'sT-shirt; sports T-shirt; couple's T-shirt . . . . . . . . .

During the search of the navigation dictionary, editor recommendation,the index keyword of which matching the search keyword and the indexcategory of which matching the search category, may be obtained as aresult of matching the search keyword with the index keywords of thenavigation dictionary as well as matching the search category with theindex categories of the navigation dictionary. As the editorrecommendation includes the index categories, the user may obtaindifferent recommendation results under different search categories andhence the relevancy and richness of the recommendation results may beenhanced. Additionally, the editor recommendation may includerecommendation types. A recommendation type is used to determine theformat of the recommendation content and may be represented by arecommendation type field. For example, the value “1” of therecommendation type field means to return the category and theattribute, the value “2” means to return the category only and the value“3” means to return the attribute only. A person ordinarily skilled inthe art may further set other recommendation types based on the editorrecommendation.

At 103, the server sends the one or more recommendation results to theclient terminal.

In one embodiment, after sending the recommendation results to theclient terminal, the recommendation content in the recommended resultsmay be displayed on a use interface device based on the format of therecommended type of the recommendation results in the event that theeditor recommendation includes index keyword, recommendation type andthe recommendation content.

The present disclosure has a number of advantages. As the recommendationresults include the editor recommendation that is obtained based oninformation relating to the user behavior, the relevancy, richness andintelligence of the intelligent navigation may be enhanced. The negativeeffects on the recommendation algorithm caused by noise, lack ofclicking information, and misplacement of products under wrongcategories may be minimized and, accordingly, the user shoppingexperience may be improved. As the recommendation data is provided byanalyzing user behavior information and checking the number of products,coupled with the ability to track the effect of the edited navigationdata, the accuracy, relevancy, richness and intelligence of therecommendation results are guaranteed. Accordingly, user search timedecreases, and search loading on the servers is reduced. Of course, anyproduct implementing an embodiment of the present disclosure needs notto possess all the aforementioned advantages at the same time.

The method of intelligent navigation of the present disclosure may beimplemented in a system the structure of which is shown in FIG. 2. Thesystem includes a client terminal, a webpage server, a log server, astorage calculation system, a back-end database, editing device and anintelligent navigation server. The client terminal sends user inquiriesto the webpage server to obtain recommendations from the webpage server.The webpage server obtains recommendations from the intelligentnavigation server and returns the recommendations to the clientterminal. The log server obtains user behavior information from thewebpage server and generates the user behavior log, which includes userinquiries and a record of product clicks. The storage calculation systemmay be a system of distributed file storage and calculation that storesthe user behavior log, generates the algorithmic recommendation usingthe intelligent navigation algorithm, and computes the statistics of thereference data for the editing device. The back-end database stores thealgorithmic recommendation and the reference data. The editing deviceedits the obtained editor recommendation information, displays theeditor recommendations and the reference data, and provides an interfacefor modifying the editor recommendations.

A detailed description of the method of intelligent navigation will begiven in conjunction with the above scenarios.

FIG. 3 is a flowchart of a method of intelligent navigation inaccordance with another embodiment of the present disclosure. The methodincludes a number of operations as described below.

At 301, a log server obtains user behavior information from a webpageserver, generates a user behavior log based on the user behaviorinformation, and stores the user behavior log in a storage calculationsystem.

In one embodiment, the log server generates records in different formatsbased on the different behaviors of one or more users, and stores therecords in the storage calculation system in text form. The text form ofthe records forms the user behavior log, and the storage calculationsystem may be a distributed storage calculation system.

The user behavior log may include a search log, a product clicking log,a purchase log and a navigation area clicking log. The search log mayinclude the times of search, the user-entered keywords and thecategories under which searches were conducted. The product clicking logmay include the times of clicks, the user-entered keywords, the productsclicked on by one or more users after searching, and the category andattributes of each product. The purchase log may include the times ofpurchase, the user-entered keywords for searches, the products purchasedby one or more users after searching, and the category and attributes ofeach product. The navigation area clicking log may include the times ofclicks in the navigation area, the user-entered keywords for searches,and the categories clicked on by one or more users in the recommendedcategories after searching.

At 302, the storage calculation system calculates an algorithmicrecommendation corresponding to a keyword by using an intelligentnavigation algorithm, generates reference data based on statistics ofinformation recorded in the user behavior log, and provides thealgorithmic recommendation and the reference data to a back-enddatabase.

In one embodiment, after the log server provides the user behavior logto the storage calculation system, from time to time the storagecalculation system may periodically calculate a respective algorithmicrecommendation corresponding to each keyword. Each keyword may be usedas the preset input of the algorithm for calculation by the algorithm.Accordingly, the corresponding algorithmic recommendation may beobtained. The algorithmic recommendation may include algorithmicrecommended categories and/or algorithmic recommended attributes.

In another embodiment, the reference data generated by the storagecalculation system may include a user inquiry record, a product clickingrecord, a navigation area clicking record, a purchase record andinformation of a category tree of products. The storage calculationsystem may periodically gather the statistics of the distribution ofsearch categories corresponding to each keyword, the distribution ofcategories of product clicks, and the distribution of purchases based onthe user behavior log, and then present the distributions in ahierarchical structure of a category tree. In one embodiment, thestorage calculation system may normalize the keywords recorded in thesearch log (including, for example, conversion between capital and smallcases, full/half-width conversion for entry of Chinese characters,punctuation conversion, conversion of traditional/simplified Chinesecharacter fonts, etc.), and the statistics of the search page view (PV)and search user view (UV) of each keyword in different categories may begathered. The keywords recorded in the product clicking log may benormalized, and the statistics of the clicking PV and the clicking UV ofeach keyword in different categories may be gathered. The statistics ofthe purchase log may be gathered by calculating the transaction UV, thenumber of transactions and the monetary amount of transactions of eachkeyword in different categories. Then, the storage calculation mayintersect the above data obtained by using the combination“keyword+search category” as a key to process the search log, theproduct clicking log and the purchase log to obtain reference data inthe form of “keyword+search category+search PV+search UV+clickingPV+clicking UV+transaction UV+number of transactions+monetary amount oftransactions” or in a similar format.

As certain structural relationship exists among the search categories,the search categories corresponding to a given keyword may be includedin the tree nodes or a similar structure. For example, if the keyword is“T-shirt”, the corresponding search categories may be included in thetree nodes of the category tree of products as shown in FIG. 4.

The storage calculation system may periodically calculate and presentinformation relating to user feedback corresponding to “keyword+searchcategory”. The information relating to user feedback may include thedistribution of clicks of recommended categories and the distribution ofclicks of all categories. Specifically, the storage calculation systemmay provide the statistics of the clicks of recommended categoriescorresponding to “keyword+search category” based on the clicking log ofthe navigation area, calculate the categorical clicks of all categories,and combine the reference data to generate the user feedbackinformation, as shown in Table 4.

TABLE 4 Keywords Search Recommended Categories Click 1 CategoriesCategory 1 of all categories Click 2 Category 2 of all categories Click3 . . . . . . Category n of all categories Click n + 1

In another embodiment, the storage calculation system may periodicallyprovide high high-PV keywords to the back-end database and maintain thehigh-PV keyword status. The status is indicative of whether the high-PVkeywords have been edited and still time-effective.

At 303, an editing device compiles the reference data in the back-enddatabase and obtains the editor recommendation corresponding to thekeywords.

In the editing environment of the editing device, imported keywordlabels and managed keyword labels are presented. The imported keywordlabels import a list of keywords in a text document into an editingpage. The managed keyword labels may be used to manage the pages todisplay a summary of keywords and have filtering function (for example,only keywords of certain status may be displayed). After entering theediting page by choosing a keyword, a display of the algorithmicrecommendation provided by the intelligent navigation, the distributionof search categories and the distribution of clicking categories, etc.may be provided.

The editing page may provide the editing and checking functions. Theediting function may include modification of the title of thealgorithmic recommendation information to obtain the editorrecommendation, and adding the recommended categories and recommendedattributes corresponding to keywords for the editor recommendation. Forexample, when the algorithmic recommendation is “T-shirt+couple's wear”and the keyword is “T-shirt”, the algorithmic recommendation may bemodified as “T-shirt+couple's T-shirt”, and the recommended category“sports T-shirt” corresponding to the keyword “T-shirt” may also beadded. The checking function may include preview of the online displayof the editor recommendation when editing the pages in order to comparedifferent editor recommendations. For example, when the editorrecommendation includes “T-shirt+couple's T-shirt” and “T-shirt+sportsT-shirt”, the online display of the two pieces of editor recommendationmay be previewed, so as to compare the two pieces of editorrecommendation for determination of whether to keep the two pieces ofeditor recommendation.

The editing device further provides the distribution of searchcategories, the distribution of clicking categories and the status oftransactions under all categories for determining the recommendedcategories and whether to edit and provide data on the downward path ofcategories. The editing page may dynamically present the number ofproducts of each search category to avoid leading the user to categorieshaving a very small number of products. The editing device may check thedistribution of clicks of keywords in the navigation area and in allcategories during a pre-determined period of time based on the referencedata, obtain edited user feedback, and provide the basis for determiningediting rules.

At 304, the storage calculation system integrates the algorithmicrecommendation and the editor recommendation to result in integrateddata, compiles the integrated data into the navigation dictionary, andstores the navigation dictionary in an intelligent navigation server.

The editor recommendation may be compiled in the format of “indexkeywords+index categories+recommendation type+recommendation content.”An example of the editor recommendation is shown as Table 5.

TABLE 5 Index Index Recommenda- Keywords Categories tion TypeRecommendation content T-shirt Root 1 Women's long-sleeve T-shirt;category Women's short-sleeve T-shirt; Men's long-sleeve T-shirt; Men'sshort-sleeve T-shirt; Children's T-shirt; sports T-shirt; couple'sT-shirt . . . . . . . . . . . .

In one embodiment, the recommendation content is used for determiningthe recommended categories and/or recommended attributes shown on a userinterface. The recommendation type is used for determining the format ofthe recommendation content. The recommendation type may include“category+feature”, “category” and “feature”, etc., and differentrecommendation types may be represented by different notations, such asnumbers 1, 2 and 3 which indicate different recommendation types. Basedon the above data structure, a key-value inquiry mechanism, in which the“key” is “index keyword+index category” and the “value” is“recommendation type+recommendation content” or “recommended category”,may be established for the navigation dictionary.

The index words may be time-sensitive keywords. The recommendationcontent corresponding to time-sensitive keywords is time-sensitive andis relevant to time, such as seasons, dates, etc. For example, if thetime-sensitive keyword is “T-shirt”, its corresponding recommendationcontent “short-sleeve T-shirt” is time-sensitive as it has a largernumber of clicks in summer than in winter. When the recommendationcontent corresponding to the time-sensitive keywords has an amount ofclicks less than a pre-determined threshold value, or the recommendationcontent is not the category or attribute having the largest number ofclicks, the recommendation content is deemed to be invalid. The editingdevice may periodically provide reminder to review the recommendationcontent corresponding to time-sensitive keywords, in order for a systemoperator to determine whether the recommendation content is invalid toedit the recommendation content regularly when the recommendationcontent is invalid.

In another embodiment, when the recommendation content does not meetuser requirements, in other words, the number of clicks is smaller thanthe pre-determined threshold or it is not the category or attributehaving the largest number of clicks, the editing device may providereminder to re-edit the recommendation content. Specifically, in theuser feedback information as shown in FIG. 2, if a certain category hasa number of clicks larger than one of the recommended categories, thesystem operator may be reminded to check whether the number of clicks ofthat category is fraudulent data. If the number of clicks of thatcategory is not fraudulent data, it is determined that the currentrecommendation categories cannot meet the requirements and that propermodifications to the recommendation categories are necessary. Forexample, a category corresponding to a given index keyword but havingthe largest number of clicks may be used to replace the currentrecommendation content, or the editor recommendation corresponding tothe recommendation categories may be deleted from the navigationdictionary.

At 305, a client terminal sends a user inquiry to the webpage server.

The user inquiry may include search keywords and search categories. Thesearch categories may include the root category by default, and may bedetermined as a certain tree node in the category tree of products asshown in FIG. 4 based on user choice.

At 306, the webpage server requests for recommendation content from theintelligent navigation server based on the user inquiry.

At 307, the intelligent navigation server searches the navigationdictionary based on the user inquiry. If a recommendation resultcorresponding to the user inquiry is found in the navigation dictionary,the method proceeds to operation 308; otherwise the method proceeds tooperation 309.

At 308, the intelligent navigation server sends the requestedrecommendation result to the client terminal through the webpage server.

In one embodiment, if the intelligent navigation server uses the searchkeyword and the search category as the “key” and finds the corresponding“value” in the navigation dictionary, the intelligent navigation servermay return the recommendation content in the “value” as therecommendation result to the webpage server. The webpage server sendsthe recommendation result to the client terminal client terminal wherethe recommendation result is presented to the user.

For example, when the user enters the keywords “mobile phone” in theroot category through the client terminal and conducts a categorysearch, the intelligent navigation server returns the recommendationresult through the webpage server and presents the result on the userinterface of the client terminal, as shown in FIG. 5. In the depictedexample, the recommendation result includes “mobile phone (439376)”,“domestic boutique mobile phone (222178)” and “3C digital accessorymarket (1221425)”, which are the recommended categories.

In another embodiment, the recommended result presented on the clientterminal may be in the form of a parent-sub category, as shown in FIG.6. When the user enters the keyword “T-shirt” in the root categorythrough the client terminal and conducts a category search, therecommendation result is presented as three separate parent-categories,namely “women's wear”, “men's wear” and “others”, and each of theparent-categories may include a number of sub-categories.

In an alternative embodiment, the recommendation result presented on theclient terminal may be the recommended attribute, as shown in FIG. 7.When the user enters the keywords “mobile phone” in the root categorythrough the client terminal and conducts a category search, therecommendation result is presented based on the attributes of “brands”,“appearances”, “price ranges”, “smart phone” and “networks”.

At 309, the intelligent navigation server returns the recommendationresult based on the number of products.

In one embodiment, if the intelligent navigation server uses the searchkeyword and the search category as the “key” and does not find anycorresponding “value” in the navigation dictionary, the intelligentnavigation server may return the recommendation content based onkeywords of the titles of online products and the number of products.

For example, when the user enters the keyword “plate” in the rootcategory through the client terminal and conducts a category search, andthe intelligent navigation server does not find any correspondingrecommendation, it presents the top ten categories that have the largestnumber of products based on the keywords of the title of online productsand displays the number of products corresponding to each category.

The present disclosure provides a number of advantages. As therecommendation results include the editor recommendation which isobtained based on information relating to the user behavior, therelevancy, richness and intelligence of the intelligent navigation maybe enhanced. The negative effects on the recommendation algorithm causedby noise, lack of clicking information and misplacement of productsunder wrong categories may be minimized and, accordingly, the usershopping experience may be improved. As the recommendation data isprovided by analyzing user behavior information and checking the numberof products, coupled with the ability to track the effect of the editednavigation data, the accuracy, relevancy, richness and intelligence ofthe recommendation results are guaranteed. Accordingly, user search timedecreases, and search loading on the servers is reduced. Of course, anyproduct implementing an embodiment of the present disclosure needs notto possess all the aforementioned advantages at the same time.

The embodiments of the present disclosure provide a method ofintelligent navigation and certain implementation scenarios.Correspondingly, the present disclosure also provides an apparatus and asystem using the aforementioned method, described below.

FIG. 8 illustrates a block diagram of an intelligent navigation serverin accordance with an embodiment of the present disclosure. Theintelligent navigation server includes a number of components asdescribed below.

An acquisition module 810 is used for receiving user inquiries from aclient terminal.

An inquiry module 820 is used for searching a navigation dictionarybased on the user inquiries received by the acquisition module 810 andfor obtaining recommendation results corresponding to the userinquiries. The navigation dictionary includes editor recommendationsbased on user behavior.

A user inquiry may include one or more search keywords. The editorrecommendation may include one or more index keywords and content of therecommendation. The inquiry module 820 searches for the editorrecommendation in the navigation dictionary based on the one or moresearch keywords, obtains the search categories and content of therecommendation corresponding to the one or more search keywords, andprovides the content of the recommendation as the recommended result.

The user inquiry may further include one or more search keywords andsearch categories. The editor recommendation may include one or moreindex keywords, one or more index categories and recommendation content.The inquiry module 820 searches for the editor recommendation in thenavigation dictionary based on the one or more search keywords andsearch categories, obtains the recommendation content corresponding tothe one or more search keywords and search categories, and provides thecontent of the recommendation as the recommended result.

The user inquiry may further include one or more search keywords. Theeditor recommendation may include one or more index keywords,recommendation types and recommendation content. The inquiry module 820searches for the editor recommendation in the navigation dictionarybased on the one or more search keywords and search categories, obtainsthe recommendation content corresponding to the one or more searchkeywords and search categories, and provides the recommendation typesand the content of the recommendation as the recommended result.

A transmission module 830 is used for sending the recommendation result,obtained by the inquiry module 820, to the client terminal.

The present disclosure provides a number of advantages. As therecommendation results include the editor recommendation which isobtained based on information relating to the user behavior, therelevancy, richness and intelligence of the intelligent navigation maybe enhanced. The negative effects on the recommendation algorithm causedby noise, lack of clicking information and misplacement of productsunder wrong categories may be minimized and, accordingly, the usershopping experience may be improved. As the recommendation data isprovided by analyzing user behavior information and checking the numberof products, coupled with the ability to track the effect of the editednavigation data, the accuracy, relevancy, richness and intelligence ofthe recommendation results are guaranteed. Accordingly, user search timedecreases, and search loading on the servers is reduced. Of course, anyproduct implementing an embodiment of the present disclosure needs notto possess all the aforementioned advantages at the same time.

FIG. 9 illustrates a block diagram of a system of intelligent navigationin accordance with another embodiment of the present disclosure. Thesystem includes a number of components as described below.

A webpage server is used for receiving user inquiries from a clientterminal, sending the user inquiries to an intelligent navigationserver, receiving recommendation result returned from the intelligentnavigation server, and sending the recommendation result to the clientterminal.

The intelligent navigation server is used for receiving the userinquiries from the webpage server, searching a navigation dictionarybased on the user inquiries, obtaining recommendation resultscorresponding to the user inquiries, and sending the recommendationresults to the webpage server. The navigation dictionary includes editorrecommendation based on user behavior.

A user inquiry may include one or more search keywords. The editorrecommendation may include one or more index keywords and content of therecommendation. The intelligent navigation server searches for theeditor recommendation in the navigation dictionary based on the one ormore search keywords, obtains the recommendation content correspondingto the one or more search keywords, and provides the content of therecommendation as the recommendation result.

The user inquiry may further include one or more search keywords andsearch categories. The editor recommendation may include one or moreindex keywords, one or more index categories and recommendation content.The intelligent navigation server searches for the editor recommendationin the navigation dictionary based on the one or more search keywordsand search categories, obtains the recommendation content correspondingto the one or more search keywords and search categories, and providesthe content of the recommendation as the recommended result.

The user inquiry may further include one or more search keywords. Theeditor recommendation may include one or more index keywords,recommended types and recommendation content. The intelligent navigationserver searches for the editor recommendation in the navigationdictionary based on the one or more search keywords, obtains therecommended types and the recommendation content corresponding to theone or more search keywords, and provides the content of therecommendation as the recommended result.

Further, as shown in FIG. 10, the system may also include a number ofadditional components as described below.

A client terminal is used for receiving the recommendation result fromthe webpage server and displaying the recommendation result on a userinterface based on the format which is determined by a recommendationtype in the recommendation result.

A log server is used for obtaining user behavior information from thewebpage server, generating a user behavior log based on the informationrelating to the user behavior, and sending the user behavior log to astorage calculation platform.

The storage calculation platform is used for storing the user behaviorlog, gathering statistics of reference data based on the user behaviorlog, calculating and obtaining algorithmic recommendation by using anintelligent navigation algorithm, importing the reference data and thealgorithmic recommendation into a back-end database, integrating theeditor recommendation and the algorithmic recommendation in the back-enddatabase, compiling the integrated database into the navigationdictionary, and importing the navigation dictionary into the intelligentnavigation server.

In one embodiment, the user behavior log may be a search log, and thereference data may include keywords, search categories and search data.The storage calculation platform is used for generating the editorrecommendation, which includes one or more index keywords, one or moreindex categories and the recommendation content, based on the referencedata when the search data of the reference data is greater than apredetermined search threshold. An index keyword is a keyword of thereference data. An index category is a search category of the referencedata. The recommendation content is the category and/or attributecorresponding to the search data of the reference data having thelargest value.

In another embodiment, the user behavior log may be a clicking log, andthe reference data may include keywords, search categories and clickingdata. The storage calculation platform is used for generating the editorrecommendation, which includes one or more index keywords, one or moreindex categories and the recommendation content, based on the referencedata when the clicking data of the reference data is greater than apredetermined click threshold. An index keyword is a keyword of thereference data. An index category is a search category of the referencedata. The recommendation content is the category and/or attributecorresponding to the clicking data of the reference data having thelargest value.

In an alternative embodiment, the user behavior log may be a purchaselog, and the reference data may include keywords, search categories andpurchase data. The storage calculation platform is used for generatingthe editor recommendation, which includes one or more index keywords,one or more index categories and the recommendation content, based onthe reference data when the purchase data of the reference data isgreater than a predetermined purchase threshold. An index keyword is akeyword of the reference data. An index category is a search category ofthe reference data. The recommendation content is the category and/orattribute corresponding to the purchase data of the reference datahaving the largest value.

The editing device is used for editing based on the reference data inthe back-end database to obtain the editor recommendation correspondingto the user behavior, and importing the editor recommendation into theback-end database.

The editing device may also be used for obtaining information relatingto user feedback corresponding to the editor recommendation, anddetermining whether the editor recommendation is valid based on the userfeedback. In the event that the editor recommendation is valid, theeditor recommendation will be kept; otherwise, it will be modified.

The user feedback information includes the number of clickscorresponding to the editor recommendation and the number of clicks ofeach category of all the categories. In one embodiment, the editingdevice is used for determining that the editor recommendation is invalidwhen the number of clicks corresponding to the editor recommendation issmaller than the number of clicks of a certain category of allcategories.

The present disclosure provides a number of advantages. As therecommendation results include the editor recommendation which isobtained based on information relating to the user behavior, therelevancy, richness and intelligence of the intelligent navigation maybe enhanced. The negative effects on the recommendation algorithm causedby noise, lack of clicking information and misplacement of productsunder wrong categories may be minimized and, accordingly, the usershopping experience may be improved. As the recommendation data isprovided by analyzing user behavior information and checking the numberof products, coupled with the ability to track the effect of the editednavigation data, the accuracy, relevancy, richness and intelligence ofthe recommendation results are guaranteed. Accordingly, user search timedecreases, and search loading on the servers is reduced. Of course, anyproduct implementing an embodiment of the present disclosure needs notto possess all the aforementioned advantages at the same time.

Through the above-described preferred embodiments, persons of ordinaryskill in the art can clearly understand that the present disclosure canbe implemented in software in combination with necessary hardwareplatforms. Alternatively the present disclosure can be implemented inhardware as well, but in most cases software implementation may bebetter than hardware implementation. Based on such understanding, thetechnical features of the present disclosure in essence can beimplemented in the form of a computer software application. The computersoftware application may be stored in one or more computer-readablestorage media and includes certain computer-executable instructionswhich can make certain terminal equipment (e.g., a mobile phone, apersonal computer, a server or a network device, etc.) to carry out themethod described in the embodiments of the present disclosure.

The present disclosure provides only a number of preferred embodiments.It should be understood persons of ordinary skill in the art thatcertain modifications and improvements can be made and should beconsidered to be within the protection of the present disclosure withoutdeparting from the principles of the present disclosure.

Persons of ordinary skill in the art can understand that the modules ofthe apparatus in the embodiments can be distributed in the apparatus ofthe embodiments, or in one or more different apparatuses different fromthe present embodiments. The modules in the embodiments can beintegrated or arranged separately, and they can be integrated as onemodule or can be further divided into multiple sub-modules. The serialnumbers shown in the embodiments of the present disclosure are merelyfor description purpose and do not intend to indicate the merits anddemerits of the embodiments.

What is claimed is:
 1. A method of intelligent navigation, comprising:receiving a user inquiry from a client terminal; searching a navigationdictionary based on the user inquiry to obtain a recommendation resultcorresponding to the user inquiry, the navigation dictionary includingan editor recommendation based on user behavior information; and sendingthe recommendation result to the client terminal.
 2. The method asrecited in claim 1, wherein the user inquiry comprises a search keyword,and wherein the editor recommendation comprises an index keyword andrecommendation content.
 3. The method as recited in claim 2, whereinsearching the navigation dictionary based on the user inquiry to obtainthe recommended result corresponding to user inquiry comprises:searching for the editor recommendation in the navigation dictionarybased on the search keyword; obtaining the recommendation contentcorresponding to the search keyword; and providing the recommendationcontent as the recommendation result.
 4. The method as recited in claim1, wherein the user inquiry comprises a search keyword and a searchcategory, and wherein the editor recommendation comprises an indexkeyword, an index category and recommendation content.
 5. The method asrecited in claim 4, wherein searching the navigation dictionary based onthe user inquiry to obtain the recommended result corresponding to userinquiry comprises: searching for the editor recommendation in thenavigation dictionary based on the search keyword and a the searchcategory; obtaining the recommendation content corresponding to thesearch keyword and the search category; and providing the recommendationcontent as the recommendation result.
 6. The method as recited in claim1, wherein the user inquiry comprises a search keyword, and wherein theeditor recommendation comprises an index keyword, a recommended type,and recommendation content.
 7. The method as recited in claim 6, whereinsearching the navigation dictionary based on the user inquiry to obtainthe recommended result corresponding to user inquiry comprises:searching for the editor recommendation in the navigation dictionarybased on the search keyword; obtaining the recommendation type andrecommendation content corresponding to the search keyword; andproviding the recommendation type and recommendation content as therecommendation result.
 8. The method as recited in claim 6, aftersending the recommendation result to the client terminal, furthercomprising: displaying the recommendation content on a user interfacebased on a format according to the recommendation type.
 9. The method asrecited in claim 1, before searching the navigation dictionary based onthe user inquiry, further comprising: obtaining the user behaviorinformation; providing a user behavior log based on the user behaviorinformation; generating reference data based on statistics in the userbehavior log; compiling the reference data to obtain the editorrecommendation corresponding to the user behavior information; andcompiling the editor recommendation into the navigation dictionary. 10.The method as recited in claim 9, wherein the user behavior logcomprises a search log, and wherein the reference data comprises akeyword, a search category, and search data.
 11. The method as recitedin claim 10, wherein compiling the reference data to obtain the editorrecommendation corresponding to the user behavior information comprises:when the search data of the reference data is greater than apredetermined search threshold, generating the editor recommendationwhich includes an index keyword, an index category, and recommendationcontent based on the reference data, wherein the index keyword comprisesa keyword of the reference data, the index category comprises a searchcategory of the reference data, and the recommendation content comprisesa category, an attribute, or the category and attribute corresponding tothe search data having a greatest value in the reference data.
 12. Themethod as recited in claim 9, wherein the user behavior log comprises aclicking log, and wherein the reference data comprises a keyword, asearch category, and clicking data.
 13. The method as recited in claim12, wherein compiling the reference data to obtain the editorrecommendation corresponding to the user behavior information comprises:when the clicking data of the reference data is greater than apredetermined clicking threshold, generating the editor recommendationwhich includes an index keyword, an index category, and recommendationcontent based on the reference data, wherein the index keyword comprisesa keyword of the reference data, the index category comprises a searchcategory of the reference data, and the recommendation content comprisesa category, an attribute, or the category and attribute corresponding tothe clicking data having a greatest value in the reference data.
 14. Themethod as recited in claim 9, wherein the user behavior log comprises apurchase log, and wherein the reference data comprises a keyword, asearch category, and purchase data.
 15. The method as recited in claim14, wherein compiling the reference data to obtain the editorrecommendation corresponding to the user behavior information comprises:when the purchase data of the reference data is greater than apredetermined purchase threshold, generating the editor recommendationwhich includes an index keyword, an index category, and recommendationcontent based on the reference data, wherein the index keyword comprisesa keyword of the reference data, the index category comprises a searchcategory of the reference data, and the recommendation content comprisesa category, an attribute, or the category and attribute corresponding tothe purchase data having a greatest value in the reference data.
 16. Themethod as recited in claim 9, after compiling the reference data toobtain the editor recommendation corresponding to the user behaviorinformation, further comprising: obtaining user feedback informationcorresponding to the editor recommendation; determining whether or notthe editor recommendation is valid; keeping the editor recommendation ifit is determined to be valid; and modifying the editor recommendation ifit is determined to be invalid.
 17. The method as recited in claim 16,wherein the user feedback information comprises a number of clickscorresponding to the editor recommendation and a number of clicks of agiven category, and wherein when the number of clicks corresponding tothe editor recommendation is smaller than the number of clicks of thegiven category, the editor recommendation information is determined tobe valid.
 18. An intelligent navigation server, comprising: an obtainingmodule that receives a user inquiry from a client terminal; an inquirymodule that searches a navigation dictionary based on the user inquiryto obtain a recommendation result corresponding to the user inquiry, thenavigation dictionary including an editor recommendation which isobtained based on user behavior; and a transmission module that sendsthe recommendation result to the client terminal.
 19. An intelligentnavigation system, comprising: a navigation dictionary including aneditor recommendation based on user behavior information; a webpageserver that receives a user inquiry from a client terminal, sends theuser inquiry to an intelligent navigation server, receives arecommendation result from the intelligent navigation server, and sendsthe recommendation result to the client terminal; and the intelligentnavigation server that receives the user inquiry information from thewebpage server, searches the navigation dictionary based on the userinquiry to obtain the recommendation result corresponding to the userinquiry information, and sends the recommendation result to the webpageserver.
 20. The system as recited in claim 19, further comprising: a logserver that obtains the user behavior information from the webpageserver, generates a user behavior log based on the user behaviorinformation, and sends the user behavior log to a storage calculationplatform; the storage calculation platform that stores the user behaviorlog, generates reference data based on statistics in the user behaviorlog, calculates an algorithmic recommendation using an intelligentnavigation algorithm, provides the reference data and algorithmicrecommendation to a back-end database, integrates the editorrecommendation and the algorithmic recommendation to provide integrateddata, compiles the integrated data into the navigation dictionary, andimports the navigation dictionary into the intelligent navigationserver; and an editing device that edits the reference data, obtains theeditor recommendation corresponding to user behavior information, andimports the editor recommendation into the back-end database.